Zobrazeno 1 - 10
of 1 286
pro vyhledávání: '"Manivasagam, A."'
Large scale 3D scene reconstruction is important for applications such as virtual reality and simulation. Existing neural rendering approaches (e.g., NeRF, 3DGS) have achieved realistic reconstructions on large scenes, but optimize per scene, which i
Externí odkaz:
http://arxiv.org/abs/2409.19405
Autor:
Yang, Ze, Chen, George, Zhang, Haowei, Ta, Kevin, Bârsan, Ioan Andrei, Murphy, Daniel, Manivasagam, Sivabalan, Urtasun, Raquel
Self-driving vehicles (SDVs) require accurate calibration of LiDARs and cameras to fuse sensor data accurately for autonomy. Traditional calibration methods typically leverage fiducials captured in a controlled and structured scene and compute corres
Externí odkaz:
http://arxiv.org/abs/2409.18953
Autor:
Pun, Ava, Sun, Gary, Wang, Jingkang, Chen, Yun, Yang, Ze, Manivasagam, Sivabalan, Ma, Wei-Chiu, Urtasun, Raquel
Different outdoor illumination conditions drastically alter the appearance of urban scenes, and they can harm the performance of image-based robot perception systems if not seen during training. Camera simulation provides a cost-effective solution to
Externí odkaz:
http://arxiv.org/abs/2312.06654
Autor:
Liu, Jeffrey Yunfan, Chen, Yun, Yang, Ze, Wang, Jingkang, Manivasagam, Sivabalan, Urtasun, Raquel
We propose a new method for realistic real-time novel-view synthesis (NVS) of large scenes. Existing neural rendering methods generate realistic results, but primarily work for small scale scenes (<50 square meters) and have difficulty at large scale
Externí odkaz:
http://arxiv.org/abs/2311.05607
Reconstructing objects from real world data and rendering them at novel views is critical to bringing realism, diversity and scale to simulation for robotics training and testing. In this work, we present NeuSim, a novel approach that estimates accur
Externí odkaz:
http://arxiv.org/abs/2311.05602
Autor:
Wang, Jingkang, Manivasagam, Sivabalan, Chen, Yun, Yang, Ze, Bârsan, Ioan Andrei, Yang, Anqi Joyce, Ma, Wei-Chiu, Urtasun, Raquel
Realistic simulation is key to enabling safe and scalable development of % self-driving vehicles. A core component is simulating the sensors so that the entire autonomy system can be tested in simulation. Sensor simulation involves modeling traffic p
Externí odkaz:
http://arxiv.org/abs/2311.01447
Self-driving vehicles (SDVs) must be rigorously tested on a wide range of scenarios to ensure safe deployment. The industry typically relies on closed-loop simulation to evaluate how the SDV interacts on a corpus of synthetic and real scenarios and v
Externí odkaz:
http://arxiv.org/abs/2311.01446
Autor:
Yang, Ze, Chen, Yun, Wang, Jingkang, Manivasagam, Sivabalan, Ma, Wei-Chiu, Yang, Anqi Joyce, Urtasun, Raquel
Rigorously testing autonomy systems is essential for making safe self-driving vehicles (SDV) a reality. It requires one to generate safety critical scenarios beyond what can be collected safely in the world, as many scenarios happen rarely on public
Externí odkaz:
http://arxiv.org/abs/2308.01898
Autor:
Justina Michael, Thenmozhi Manivasagam
Publikováno v:
PeerJ Computer Science, Vol 10, p e2518 (2024)
Extracting the essential features and learning the appropriate patterns are the two core character traits of a convolution neural network (CNN). Leveraging the two traits, this research proposes a novel feature extraction framework code-named ‘Hier
Externí odkaz:
https://doaj.org/article/f4212d89b2294af2bf54e8de3775f8ce
Autor:
Pearlin Amaan Khan, Ansheed Raheem, Cheirmadurai Kalirajan, Konda Gokuldoss Prashanth, Geetha Manivasagam
Publikováno v:
ACS Materials Au, Vol 4, Iss 5, Pp 479-488 (2024)
Externí odkaz:
https://doaj.org/article/fbb89b54cbbe49718d50ed1c43096f94